This invention relates to a method and device for phase coherent underwater acoustic communications. More particularly, the invention relates to a system for long range underwater acoustic communications using a combination of arrays and sub-arrays.
The ocean presents an acoustic communication channel, which is band-limited and temporally variable. Propagation in the horizontal can be severely influenced by macro and micro multipath variability. Vertical propagation is often less severely impacted by the multipath.
Incoherent communication schemes, using for example frequency shift keying algorithms, are used for line of sight propagation conditions in which multipath has minimal impact on the signals of interest. At long ranges, symbol rates for incoherent communications are limited by the multipath symbol interference. Additional processing (such as error encoding) is often required to remove the bit errors (due to symbol interference). The available frequency band is limited by frequency fading.
Coherent communication schemes use the available bandwidth more efficiently and provide higher data rates than the incoherent schemes for horizontal transmission of signals in a multipath environment. The state of the art systems use a (recursive) minimum least-mean-square (MLMS) approach for equalizing and updating the channel. The MLMS approach requires a certain minimum signal-to-noise ratio (SNR) at the receivers, of typically 10-15 dB. Maximum data rate and minimum bit error rate depend critically on the temporal properties of the channel impulse response function. The recursive least square (RLS) algorithm is computationally intensive and only a limited number (typically  less than 4) of channels can be supported by prototype systems for real time communications.
An algorithm for phase coherent acoustic communications is described in U.S. Pat. Nos. 5,301,167 and 5,844,951, incorporated herein by reference. The latter patent extends the algorithm from a single receiver to multiple receivers; it uses jointly a phase locking loop and channel equalizer to adaptively correct for the channel temporal variation to minimize bit errors. The communication signals are transmitted by grouping symbols into packets. Each packet begins with a short pulse (e.g., a Barker code of 13 symbols of binary phases) used for symbol synchronization and an initial estimate of the multipath arrival structure. It is followed by a data packet beginning with a training data set with known symbols to estimate the carrier frequency (shift) and train the equalizer. The equalizer is updated by estimating the symbol errors using either the known symbol as in the training data or a decision on the received symbol. The number of tap coefficients is estimated from the impulse response deduced from the probe/trigger pulse. Carrier frequency is estimated from the training data. The data are fractionally sampled, typically 2 samples per symbol, and the most popular schemes for signal modulation are binary phase shift keying (BPSK) and/or quadrature phase shift keying (QPSK) signals. Channel impulse response and equalizer update requires a minimal input signal-to-noise (SNR) ratio for minimal bit errors. Multiple receivers using spatial diversity are often required for successful communications.
The underwater acoustic communication channel is different from the RF channel in three respects: (1) the long multipath delay due to sound refraction and long duration of reverberation from the ocean boundary; (2) the severe signal fading due to time-variable transmission loss; and (3) the high Doppler spread/shift, i.e., the variability and offset of receiver frequency and phase relative to the transmitter resulting from the media and/or platform motion. The Doppler spread determines the signal coherence time assuming that the equalizer is able to update itself within the given coherence time. Because of these differences, the various techniques for radio frequencies (RF) communications cannot be applied directly to underwater acoustic communications.
Wireless radio communications are by line of sight with some multi-paths by reflection from nearby building and structure. Multi-path interference can usually be removed by antenna beamforming using an antenna on a horizontal plane. The array configuration can be designed with element spacing and configurations based on a plane wave model: the array aperture determines the width of the beam and element spacing determines the level of the side-lobes. Multi-paths in the oceans arrive with different vertical depletion/elevation angle. Array beamforming and diversity combining techniques can be used to mitigate the signal spreading by multi-path propagation. These two techniques are based on fundamentally different principles. To combat multi-paths, a vertical array or a planner array having some vertical aperture will be required. An array must have wide spacing between elements and hence large (vertical) aperture to combat signal fading by diversity combining. An array must have close spacing between elements to achieve the array gain by coherent beamforming. How to achieve both depends on the spatial coherence of the signal, which is normally not an issue in RF communications.
Multipath delays in underwater acoustic channels can last tens to hundreds of milliseconds, causing inter-symbol interference to extend over tens to hundreds of symbols depending on the carrier frequency and symbol rate. Inter-symbol interference in RF channels is orders of magnitude less and thus easier to deal with. Doppler shift of carrier frequency in underwater acoustic channels is several orders of magnitude larger than that of the RF channel since the sound speed is many orders lower than the speed of light. Hence, carrier frequency identification and symbol synchronization are critical for underwater systems. In addition, Doppler spread is non-negligible in the underwater communication channel as sound propagates through a random ocean medium and scatters from moving surfaces.
In a random medium, signal phase and amplitude fluctuations resulting from propagation through random environments are range, source, and receiver depth- and frequency-dependent. The temporal scale of fluctuations dictates the rate of adaptation for a coherent processor. The magnitude of the fluctuations determines how well the adaptation will work. Since successful communications require a sufficient input signal-to-noise ratio (SNR), appropriate placement of the source and receiver are necessary to avoid the xe2x80x9cshadowxe2x80x9d zones (areas where transmission loss is high). Random media increase the probability of signal fading; signal fading occurs when multipath arrivals interfere destructively.
The effects of random media on acoustic communications can be grouped into five areas: (a) signal amplitude fluctuations, which affect the ability of the modem to trigger on the probing signal (e.g., Barker code) and to decode the symbols properly; (b) signal phase fluctuations, which affect the performance of the phase locked loop; (c) temporal coherence of impulse response functions, which affects the performance of the channel equalizer; (d) Doppler spread and frequency coherence bandwidth, which limit the maximum data rate of underwater acoustic communications in an ocean channel; and (e) spatial coherence of the multipath signals, which determines the optimal use of multiple receivers. The effects of the ocean acoustic environments on the performance of phase coherence communications requires an environmental adaptive approach to estimate the signal propagation and noise characteristics in a particular ocean environment to improve the communication algorithm performance.
Multi-channel data have been processed using (1) conventional/adaptive beamforming followed by channel equalization or (2) adaptive multi-channel combining with spatial diversity. The purpose of (1) conventional or adaptive beamforming can be to improve the SNR or to simplify the multipath structure (when the multipath arrivals have significant angular spread) and thereby improve the performance of the channel equalizer. Separation of the multipaths often requires high angle resolution, which can be achieved using adaptive (e.g., minimum variance distortionless response) beamforming. Adaptive beamforming can be used to null undesired arrivals (such as surface reflected returns). In one implementation, conventional array beamforming (employing techniques such as delay and sum of the element data, or phase steering of the frequency components, or eigenvector analysis of the coherent path is used to estimate the arrival angle of the incident multipath based on, for example, the initial trigger pulse (e.g., a short LFM signal). Conventional beamforming in the frequency domain, illustrated in FIG. 1, is given by
BCB(xcex8)=xcexa3nexe2x88x92ikxnsin xcex8p(xn)
where xn is the coordinate of the nth phone and is the trigger signal. The beam with the maximum power determines the main arrival angle of the signal. The beamformed output is often processed with the single channel decision feedback equalizer since only one array is involved.
An implementation of adaptive beamforming that employs a complex FIR filter in the base band with filter tap coefficients estimated/updated based on a decision directed tracking (using, for example, the MLMS criterion) is shown in FIG. 2. In the case of high signal-to-noise (SNR) input signals, the optimum weights, in terms of MLMS error, are those that define the optimum angular response of the array (e.g., nulls in the direction of the interferers). However, the MLMS algorithm is known to have difficulties in low input SNR cases as decisions often encounter large errors. On the other hand, the delay and sum beamforming is based on the prior knowledge of the array element spacing, and it works even for low SNR input signals.
Conventional and adaptive beamforming requires that the signals are coherent between the sensors. To avoid spatial aliasing, the phones should be spaced at close to half wavelength or less of the acoustic signal. To eliminate multipath arrivals, the array must have a sufficiently large aperture to provide the necessary angular resolution to separate the multipath arrivals. But if the angle of the multipath arrivals changes rapidly over a relatively small number of symbols, the beamformer may lack the ability to track the optimum angular response as in the case of decision directed tracking due to its slow convergence rate. The MLMS can be applied to an array of widely space phones. It cannot avoid the array aliasing problem.
The various output beams can be combined using the multi-channel combining algorithm of U.S. Pat. No. 5,844,951. This is referred to as beam diversity. It is not widely used because of the difficulty of tracking individual beams as a function of time. Adaptive multi-channel combining using the MLMS algorithm can be used to combat signal fading in a time varying channel, when the signal fading is unsynchronized between channels. This usually requires widely spaced elements of receivers. It is usually known as a technique of spatial diversity. Indeed, in a ducted ocean waveguide, it is unlikely that a flat wave front will be incident on all elements of the array and each element will effectively see a different channel. Fading will likely be independent between elements and a high probability exists that the signal will be well received on at least one channel.
Adaptive multi-channel combining uses a similar structure as the MLMS beamformer of FIG. 2. When the elements of the array are widely spaced array it is essentially diversity combining. When the elements of the array are closely spaced, it yields practically the same output as the delay and sum A beamformer for high SNR cases. The MLMS algorithm is in this context a simultaneous beamforming and equalization technique. As remarked before, it does not work for low SNR cases and, it has the grating lobe (array aliasing) problem when array elements are widely spaced.
An implementation of the multi-channel processor using joint phase-locked loop and decision feedback equalizer is shown in FIG. 3. As is the case of a single channel equalizer, the MLSE algorithm requires an input SNR typically greater than 10-15 dB for reliable channel updating. Multi-channel decision feedback equalizer becomes computationally prohibitive in the case of long multi-path spread ( greater than 100 symbols) and requires a sparse equalizer to reduce the number of tap coefficients. In the latter case, the channel impulse response estimate also requires high SNR for reliable estimation of the locations of the tap coefficients. Even with the sparse equalizer, multi-channel spatial diversity is limited to a few channels as the computational load becomes excessive beyond that. Computational load goes as square of the number of the channels. It can only process a selected number of channels on a vertical array.
According to the invention, an apparatus for phase coherent underwater communications includes a transmitter; a receiver, including a processor for jointly performing coherent diversity combining, carrier recovery, channel equalization and synchronization; and an antenna array that includes a plurality of antenna sub-arrays for alternately transmitting a signal generated by the transmitter, e.g. to a designated node in the communication network, when in the sound transmitting mode, and for receiving an incoming signal, e.g. from another node in the communication network. Each of the antenna sub-arrays includes a plurality of underwater sensors positioned such that each of the sensors is spaced at about one half the wavelength of the receiver/transmitter operating frequency from any adjacent sensor in the same sub-array. At least one of the sub-arrays has a centerpoint vertically spaced apart from a centerpoint of an adjacent sub-array at a distance about equal to or larger than a vertical correlation length of a received signal.
Also according to the invention, a method for conducting underwater communications includes the steps of receiving a communications signal at each sub-array; beamforming each sub-array reception to produce a beamformed signal for each sub-array, preferably using delay-and-sum processing; diversity-combining the beamformed signals coherently to form an output signal; and detecting the output signal. In establishing the desired two-way communications channel, additional steps then include establishing a desired direction for projecting a transmission signal; and transmitting the transmission signal in the desired direction to thereby establish the underwater communications channel.
The invention provides a reduction in the minimum number of receiver channels from a physically large number of underwater sensors and a minimum input signal-to-noise ratio at the input of the underwater sensors required for successful communication (per fixed bit error rate and baud rate).
The receiver/transmitter configuration consists of a spatially distributed sub-arrays, having a sufficient vertical aperture. The individual sub-arrays are preferably beamformed using a plane-wave delay-and-sum (or phase-steering) method rather than the MLMS method. The outputs of the sub-array beamforming are used as inputs to the diversity-combining algorithm. Thus despite a total of many elements of underwater sensors used physically, the communications algorithm (the channel equalizer) sees effectively only a small number of channels. This reduces the processing time by orders of magnitude compared with the prior art diversity combining methods applied to all or many of the physical elements of underwater sensors. The sub-arrays beamformed outputs have a sufficiently high signal-to-noise ratio needed for a reliable estimation of the channel impulse response function for setting the number and locations of the tap coefficients used in the sparse channel equalizer.
Additional features and advantages of the present invention will be set forth in, or be apparent from, the detailed description of preferred embodiments which follows.